偵測串流影片中的鏡頭轉換

偵測串流影片檔案中的鏡頭轉換。

深入探索

如需包含這個程式碼範例的詳細說明文件，請參閱下列內容：

• 鏡頭轉換

程式碼範例

Java

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

import com.google.api.gax.rpc.BidiStream;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoRequest;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoResponse;
import com.google.cloud.videointelligence.v1p3beta1.StreamingFeature;
import com.google.cloud.videointelligence.v1p3beta1.StreamingLabelDetectionConfig;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoAnnotationResults;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoConfig;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1p3beta1.VideoSegment;
import com.google.protobuf.ByteString;
import io.grpc.StatusRuntimeException;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Arrays;
import java.util.concurrent.TimeoutException;

class StreamingShotChangeDetection {

  // Perform streaming video detection for shot changes
  static void streamingShotChangeDetection(String filePath)
      throws IOException, TimeoutException, StatusRuntimeException {
    // String filePath = "path_to_your_video_file";

    try (StreamingVideoIntelligenceServiceClient client =
        StreamingVideoIntelligenceServiceClient.create()) {

      Path path = Paths.get(filePath);
      byte[] data = Files.readAllBytes(path);
      // Set the chunk size to 5MB (recommended less than 10MB).
      int chunkSize = 5 * 1024 * 1024;
      int numChunks = (int) Math.ceil((double) data.length / chunkSize);

      StreamingLabelDetectionConfig labelConfig =
          StreamingLabelDetectionConfig.newBuilder().setStationaryCamera(false).build();

      StreamingVideoConfig streamingVideoConfig =
          StreamingVideoConfig.newBuilder()
              .setFeature(StreamingFeature.STREAMING_SHOT_CHANGE_DETECTION)
              .setLabelDetectionConfig(labelConfig)
              .build();

      BidiStream<StreamingAnnotateVideoRequest, StreamingAnnotateVideoResponse> call =
          client.streamingAnnotateVideoCallable().call();

      // The first request must **only** contain the audio configuration:
      call.send(
          StreamingAnnotateVideoRequest.newBuilder().setVideoConfig(streamingVideoConfig).build());

      // Subsequent requests must **only** contain the audio data.
      // Send the requests in chunks
      for (int i = 0; i < numChunks; i++) {
        call.send(
            StreamingAnnotateVideoRequest.newBuilder()
                .setInputContent(
                    ByteString.copyFrom(
                        Arrays.copyOfRange(data, i * chunkSize, i * chunkSize + chunkSize)))
                .build());
      }

      // Tell the service you are done sending data
      call.closeSend();

      for (StreamingAnnotateVideoResponse response : call) {
        StreamingVideoAnnotationResults annotationResults = response.getAnnotationResults();
        if (response.hasError()) {
          System.out.println(response.getError().getMessage());
          System.out.format(
              "Error was occured with the following status: %s\n", response.getError());
        }
        for (VideoSegment segment : annotationResults.getShotAnnotationsList()) {
          double startTimeOffset =
              segment.getStartTimeOffset().getSeconds()
                  + segment.getStartTimeOffset().getNanos() / 1e9;
          double endTimeOffset =
              segment.getEndTimeOffset().getSeconds() + segment.getEndTimeOffset().getNanos() / 1e9;

          System.out.format("Shot: %fs to %fs\n", startTimeOffset, endTimeOffset);
        }
      }
    }
  }
}

Node.js

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const path = 'Local file to analyze, e.g. ./my-file.mp4';
const {StreamingVideoIntelligenceServiceClient} =
  require('@google-cloud/video-intelligence').v1p3beta1;
const fs = require('fs');

// Instantiates a client
const client = new StreamingVideoIntelligenceServiceClient();
// Streaming configuration
const configRequest = {
  videoConfig: {
    feature: 'STREAMING_SHOT_CHANGE_DETECTION',
  },
};
const readStream = fs.createReadStream(path, {
  highWaterMark: 5 * 1024 * 1024, //chunk size set to 5MB (recommended less than 10MB)
  encoding: 'base64',
});
//Load file content
const chunks = [];
readStream
  .on('data', chunk => {
    const request = {
      inputContent: chunk.toString(),
    };
    chunks.push(request);
  })
  .on('close', () => {
    // configRequest should be the first in the stream of requests
    stream.write(configRequest);
    for (let i = 0; i < chunks.length; i++) {
      stream.write(chunks[i]);
    }
    stream.end();
  });

const stream = client.streamingAnnotateVideo().on('data', response => {
  //Gets annotations for video
  const annotations = response.annotationResults;
  const shotChanges = annotations.shotAnnotations;
  console.log(JSON.stringify(shotChanges));
  if (shotChanges.length === 1) {
    console.log('The entire video is one shot.');
  }
  shotChanges.forEach(shot => {
    console.log(
      ` Shot: ${shot.startTimeOffset.seconds || 0}` +
        `.${(shot.startTimeOffset.nanos / 1e6).toFixed(0)}s to ${
          shot.endTimeOffset.seconds || 0
        }` +
        `.${(shot.endTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
  });
});

Python

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

from google.cloud import videointelligence_v1p3beta1 as videointelligence

# path = 'path_to_file'

client = videointelligence.StreamingVideoIntelligenceServiceClient()

# Set streaming config.
config = videointelligence.StreamingVideoConfig(
    feature=(videointelligence.StreamingFeature.STREAMING_SHOT_CHANGE_DETECTION)
)

# config_request should be the first in the stream of requests.
config_request = videointelligence.StreamingAnnotateVideoRequest(
    video_config=config
)

# Set the chunk size to 5MB (recommended less than 10MB).
chunk_size = 5 * 1024 * 1024

# Load file content.
stream = []
with io.open(path, "rb") as video_file:
    while True:
        data = video_file.read(chunk_size)
        if not data:
            break
        stream.append(data)

def stream_generator():
    yield config_request
    for chunk in stream:
        yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)

requests = stream_generator()

# streaming_annotate_video returns a generator.
# The default timeout is about 300 seconds.
# To process longer videos it should be set to
# larger than the length (in seconds) of the stream.
responses = client.streaming_annotate_video(requests, timeout=600)

# Each response corresponds to about 1 second of video.
for response in responses:
    # Check for errors.
    if response.error.message:
        print(response.error.message)
        break

    for annotation in response.annotation_results.shot_annotations:
        start = (
            annotation.start_time_offset.seconds
            + annotation.start_time_offset.microseconds / 1e6
        )
        end = (
            annotation.end_time_offset.seconds
            + annotation.end_time_offset.microseconds / 1e6
        )

        print("Shot: {}s to {}s".format(start, end))

後續步驟

如要搜尋及篩選其他 Google Cloud 產品的程式碼範例，請參閱Google Cloud 範例瀏覽器。